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ERIC Number: EJ1111395
Record Type: Journal
Publication Date: 2006-Jun
Pages: 33
Abstractor: As Provided
Reference Count: 18
ISSN: EISSN-2330-8516
Weighting Procedures and the Cluster Forming Algorithm for Delete-k Jackknife Variance Estimation for Institutional Surveys. Research Report. ETS RR-06-15
Qian, Jiahe
ETS Research Report Series, Jun 2006
Weighting and variance estimation are two statistical issues involved in survey data analysis for large-scale assessment programs such as the Higher Education Information and Communication Technology (ICT) Literacy Assessment. Because survey data are always acquired by probability sampling, to draw unbiased or almost unbiased inferences for the populations, weights are required in making use of estimators such as a Horvitz-Thompson type. Variance estimation provides the basis for reporting errors. The weighting procedure generates weights based on statistical principles that are consistent with the sampling design. The estimation of the variance from survey data uses the delete-"k" jackknife resampling replicate (JRR) approach, which can be adapted for variant institutional sampling designs and for dissimilarity in institute conditions. To form clusters of "k" cases, a merge-dilute algorithm is proposed. The algorithm merges the cases of different groups into a queue and then allocates the cases of the queue to form homogeneous clusters of required sizes. The new algorithm is applied to the ICT sample from an institute taking the 2004 fall trial assessment.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A